Efficient learning and using of topologies of neural networks in machine learning

ABSTRACT

A mechanism is described for facilitating learning and application of neural network topologies in machine learning at autonomous machines. A method of embodiments, as described herein, includes monitoring and detecting structure learning of neural networks relating to machine learning operations at a computing device having a processor, and generating a recursive generative model based on one or more topologies of one or more of the neural networks. The method may further include converting the generative model into a discriminative model.

This application claims the benefit of priority from and is acontinuation of U.S. patent application Ser. No. 15/659,853 filed Jul.26, 2017, which claims the benefit of and priority to U.S. ProvisionalPatent Application No. 62/501,794 filed May 5, 2017, the full disclosureof which is incorporated herein by reference.

FIELD

Embodiments described herein relate generally to data processing andmore particularly to facilitate efficient learning and using oftopologies of deep learning neural networks in machine learning atautonomous machines.

BACKGROUND

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data; however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (SIMT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming, Chapter 3, pages 37-51 (2013) and/or Nicholas Wilt, CUDAHandbook, A Comprehensive Guide to GPU Programming, Sections 2.6.2 to3.1.2 (June 2013).

Machine learning has been successful at solving many kinds of tasks. Thecomputations that arise when training and using machine learningalgorithms (e.g., neural networks) lend themselves naturally toefficient parallel implementations. Accordingly, parallel processorssuch as general-purpose graphic processing units (GPGPUs) have played asignificant role in the practical implementation of deep neuralnetworks. Parallel graphics processors with single instruction, multiplethread (SIMT) architectures are designed to maximize the amount ofparallel processing in the graphics pipeline. In an SIMT architecture,groups of parallel threads attempt to execute program instructionssynchronously together as often as possible to increase processingefficiency. The efficiency provided by parallel machine learningalgorithm implementations allows the use of high capacity networks andenables those networks to be trained on larger datasets.

In the last few years, deep neural networks have demonstrated increasedaccuracy in relation to a number of tasks, such as large-scale objectclassification. However, such networks typically require highcomputational power for learning its parameters and for inference, suchas using graphics processing units (GPUs) for such tasks, where anycomputational cost is regarded as a function of the network topology.The topology of a network for a given task is commonly hand-crafted bymeticulous trial-and-error and often requires intuition or simplyguessing.

More recently, attempts have been made to automatically learn thetopology; however, these methods often rely on heuristic,trial-and-error approaches, etc. Further, these conventional techniquesnecessitate label-data (that is supervised).

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example, and not by way oflimitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements. So that the manner inwhich the above recited features can be understood in detail, a moreparticular description, briefly summarized above, may be had byreference to embodiments, some of which are illustrated in the appendeddrawings. It is to be noted, however, that the appended drawingsillustrate only typical embodiments and are therefore not to beconsidered limiting of its scope, for the drawings may illustrate otherequally effective embodiments.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein.

FIG. 2A-2D illustrate a parallel processor components, according to anembodiment.

FIG. 3A-3B are block diagrams of graphics multiprocessors, according toembodiments.

FIG. 4A-4F illustrate an exemplary architecture in which a plurality ofgraphics processing units are communicatively coupled to a plurality ofmulti-core processors.

FIG. 5 is a conceptual diagram of a graphics processing pipeline,according to an embodiment.

FIG. 6 illustrates a computing device hosting a topology learning andapplication mechanism according to one embodiment.

FIG. 7 illustrates a topology learning and application mechanismaccording to one embodiment.

FIG. 8 illustrates a recursive deep generative model according to oneembodiment.

FIG. 9A illustrates inverse graphical models according to oneembodiment.

FIG. 9B illustrates an inverse model having a bi-direction connectionaccording to one embodiment.

FIG. 9C illustrates a discriminative model according to one embodiment.

FIG. 9D illustrates another embodiment of graphical models according toone embodiment.

FIG. 9E illustrates another embodiment of graphical models according toone embodiment.

FIG. 9F illustrates another embodiment of graphical models according toone embodiment.

FIG. 10 illustrates a machine learning software stack, according to anembodiment.

FIG. 11 illustrates a highly-parallel general-purpose graphicsprocessing unit, according to an embodiment.

FIG. 12 illustrates a multi-GPU computing system, according to anembodiment.

FIG. 13A-13B illustrate layers of exemplary deep neural networks.

FIG. 14 illustrates training and deployment of a deep neural network.

FIG. 15 illustrates training and deployment of a deep neural network

FIG. 16 is a block diagram illustrating distributed learning.

FIG. 17 illustrates an exemplary inferencing system on a chip (SOC)suitable for performing inferencing using a trained model.

FIG. 18 is a block diagram of an embodiment of a computer system with aprocessor having one or more processor cores and graphics processors.

FIG. 19 is a block diagram of one embodiment of a processor having oneor more processor cores, an integrated memory controller, and anintegrated graphics processor.

FIG. 20 is a block diagram of one embodiment of a graphics processorwhich may be a discreet graphics processing unit, or may be graphicsprocessor integrated with a plurality of processing cores.

FIG. 21 is a block diagram of an embodiment of a graphics processingengine for a graphics processor.

FIG. 22 is a block diagram of another embodiment of a graphicsprocessor.

FIG. 23 is a block diagram of thread execution logic including an arrayof processing elements.

FIG. 24 illustrates a graphics processor execution unit instructionformat according to an embodiment.

FIG. 25 is a block diagram of another embodiment of a graphics processorwhich includes a graphics pipeline, a media pipeline, a display engine,thread execution logic, and a render output pipeline.

FIG. 26A is a block diagram illustrating a graphics processor commandformat according to an embodiment.

FIG. 26B is a block diagram illustrating a graphics processor commandsequence according to an embodiment.

FIG. 27 illustrates exemplary graphics software architecture for a dataprocessing system according to an embodiment.

FIG. 28 is a block diagram illustrating an IP core development systemthat may be used to manufacture an integrated circuit to performoperations according to an embodiment.

FIG. 29 is a block diagram illustrating an exemplary system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment.

FIG. 30 is a block diagram illustrating an exemplary graphics processorof a system on a chip integrated circuit.

FIG. 31 is a block diagram illustrating an additional exemplary graphicsprocessor of a system on a chip integrated circuit.

DETAILED DESCRIPTION

Embodiments provide for a systematic and mathematically-sound noveltechnique for learning efficient and hardware-friendly topologies ofdeep learning networks (DNNs) that are discriminative and supervisedusing unlabeled data that is generative and unsupervised.

In one embodiment, a novel technique so offered for learning a deeplatent variable model (LVM), such as a probabilistic model. Further, inone embodiment, a novel technique is provided for converting a model,such as deep LVM, into a DNN, bridging two machine learning domains,such as probabilistic graphical models and neural networks. This allowsfor efficient and systematic ways for coping with large scale problems.

Embodiments provide for a technique for learning automatic topologiesthat: 1) learn other topologies that consists of smaller sub-topologiesthat are capable of being processed in parallel, such as a recipe forparallel and sequential execution; 2) use unlabeled data (unsupervisedlearning); 3) are mathematically sound and coherent, guaranteeing tofind an optimal solution (under some assumptions); and 4) are flexibleenough to provide a range of topologies to a single problem, wheresolutions may differ by their computational cost.

Embodiments provide for a methodological dropout, such as a trainingtrick where neurons are randomly dropped each training iteration. Thisnovel technique provides for learning a dropout schedule from thestatistics of the data, where sets of neurons are dropped jointly and ina timely fashion; for example, methodological dropout in contrast tocommonly used random dropout. This allows for knowing in advance whichneurons are to be dropped and capable of being facilitated for higherefficiency by the relevant hardware. Further, a network trained usingthis novel technique is likely to have a higher or different accuracythan when trained using a common dropout technique.

Embodiments provide for a novel technique for deep neural networkdecomposition for generating parallel and sequential execution scheduleand memory sharing with potentially per-sub-network (learnable)precision level. For example, a novel technique for decomposing a givendeep network into a set of smaller independent networks with a schedulefor execution and memory sharing. This novel technique provides for agood approximation to the initial network and provides a network thatruns very efficiently on a dedicated hardware. This novel technique isappropriate for both the training and scoring. It is contemplated thateach network may work at a learned precision (weights and activations)given the data it needs to process and the local function it needs toapproximate.

Embodiments provide for on-the-fly structure learning and update. Forexample, this novel technique allows the topology of a neural network tobe updated on-the-fly in, for example, two cases, such as: 1) when moredata becomes available over time; and 2) the statistics of the datachanges over time. This novel technique also provides for modificationof a network at various granularity levels, starting from local changesto rough global changes according to the statistical power in the data.

Embodiments provide for any resource (anytime) inference by structurelearning. For example, in one embodiment, using this novel technique, adeep network having binary stochastic neurons may be generated that isable to generate outputs (inference) with accuracy increasingdynamically over time (anytime), as necessitated. In other words, binarycalculations are performed and aggregated over time. This way, if lowaccuracy is needed, less time/compute is spent. For example, wheninferring torque levels applied to a robotic arm joints in order to moveit either accurately (e.g., placing an object) or roughly (approachingan object). Another example would be that of a car navigating withoutnearby cars or pedestrians (needed low accuracy) as opposed tonavigating in a crowded city (needed high accuracy). There are notconventional techniques to dynamically update inference and improveaccuracy/precision (anytime inference). This novel technique providesfor a solution with an ability to gradually and smoothly changeinference accuracy with respect to time/compute spent.

Embodiments further provide for a novel technique for hardware-specificdeep network architecture learning for given task constraints. Forexample, in one embodiment, this novel technique provides for generatinga deep network that meets specific architectural constrains driven byhardware characteristics (e.g., cache size, parallelism, precision,etc.) and task requirements (e.g., frame rate, latency, and accuracy).This novel technique may generate a range of networks and the user maybe allowed to choose a network that performs optimally at a specificworking point. There are not conventional techniques that canautomatically generate a network given hardware and task constraints.This novel technique provides for this functionality through auser-interactive tool.

It is to be noted that terms or acronyms like “convolutional neuralnetwork”, “CNN”, “neural network”, “NN”, “deep neural network”, “DNN”,“recurrent neural network”, “RNN”, and/or the like may beinterchangeably referenced throughout this document. Further, terms like“autonomous machine” or simply “machine”, “autonomous vehicle” or simply“vehicle”, “autonomous agent” or simply “agent”, “autonomous device” or“computing device”, “robot”, and/or the like, may be interchangeablyreferenced throughout this document.

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

In the following description, numerous specific details are set forth.However, embodiments, as described herein, may be practiced withoutthese specific details. In other instances, well-known circuits,structures and techniques have not been shown in detail in order not toobscure the understanding of this description.

System Overview I

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment, the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment, the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment,the one or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that an include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment, the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O Hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s), 112 memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment, at least a portion of the componentsof the computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1 . For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1 , according to an embodiment.

In one embodiment, the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment, the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment, the front end 208couples with a scheduler 210, which is configured to distribute commandsor other work items to a processing cluster array 212. In oneembodiment, the scheduler 210 ensures that the processing cluster array212 is properly configured and in a valid state before tasks aredistributed to the processing clusters of the processing cluster array212.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212.

In one embodiment, different clusters 214A-214N of processing clusterarray 212 can be allocated for processing different types of programs orfor performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment, theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment, the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example, afirst portion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation, the number of partition units 220A-220N isconfigured to be equal to the number of memory units, such that a firstpartition unit 220A has a corresponding first memory unit 224A, a secondpartition unit 220B has a corresponding memory unit 224B, and an Nthpartition unit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment, the memory crossbar 216 hasa connection to the memory interface 218 to communicate with the I/Ounit 204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment, the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample, and in one embodiment, some instances of the parallelprocessing unit 202 can include higher precision floating point unitsrelative to other instances. Systems incorporating one or more instancesof the parallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment, the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Dirty updates can also be sent to theframe buffer via the frame buffer interface 225 for opportunisticprocessing. In one embodiment, the frame buffer interface 225 interfaceswith one of the memory units in parallel processor memory, such as thememory units 224A-224N of FIG. 2A (e.g., within parallel processormemory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations, such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments, the ROP 226 includes compression logic tocompress z or color data that is written to memory and decompress z orcolor data that is read from memory. In some embodiments, the ROP 226 isincluded within each processing cluster (e.g., cluster 214A-214N of FIG.2A) instead of within the partition unit 220. In such embodiment, readand write requests for pixel data are transmitted over the memorycrossbar 216 instead of pixel fragment data.

The processed graphics data may be displayed on a display device, suchas one of the one or more display device(s) 110 of FIG. 1 , routed forfurther processing by the processor(s) 102, or routed for furtherprocessing by one of the processing entities within the parallelprocessor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment, theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2A. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2A and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of an SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed vis the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic may be provided. The functional logic supports a varietyof operations including integer and floating point arithmetic comparisonoperations, Boolean operations bit-shifting, and computation of variousalgebraic functions. In one embodiment, the same functional-unithardware can be leveraged to perform different operations and anycombination of functional units may be present.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment, multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment, the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 308) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2A) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 308.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2A. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile (talk more about tiling) andoptionally a cache line index. The MMU 245 may include addresstranslation lookaside buffers (TLB) or caches that may reside within thegraphics multiprocessor 234 or the L1 cache or processing cluster 214.The physical address is processed to distribute surface data accesslocality to allow efficient request interleaving among partition units.The cache line index may be used to determine whether a request for acache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2A). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment, the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 324. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 324. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 324.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 324. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example, and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment, the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 324 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 324to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIGS. 3A-3B illustrate additional graphics multiprocessors, according toembodiments. The illustrated graphics multiprocessors 325, 350 arevariants of the graphics multiprocessor 234 of FIG. 2C. The illustratedgraphics multiprocessors 325, 350 can be configured as a streamingmultiprocessor (SM) capable of simultaneous execution of a large numberof execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPUcore 338A-338B) and multiple sets of load/store units 340A-340B. In oneembodiment, the execution resource units have a common instruction cache330, texture and/or data cache memory 342, and shared memory 346. Thevarious components can communicate via an interconnect fabric 327. Inone embodiment, the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory362. In one embodiment, the execution resources 356A-356D can share aninstruction cache 354 and shared memory 362, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2A, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments, a parallel processor or GPGPU as described hereinis communicatively coupled to host/processor cores to accelerategraphics operations, machine-learning operations, pattern analysisoperations, and various general purpose GPU (GPGPU) functions. The GPUmay be communicatively coupled to the host processor/cores over a bus orother interconnect (e.g., a high-speed interconnect such as PCIe orNVLink). In other embodiments, the GPU may be integrated on the samepackage or chip as the cores and communicatively coupled to the coresover an internal processor bus/interconnect (i.e., internal to thepackage or chip). Regardless of the manner in which the GPU isconnected, the processor cores may allocate work to the GPU in the formof sequences of commands/instructions contained in a work descriptor.The GPU then uses dedicated circuitry/logic for efficiently processingthese commands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440-443 (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440-443 support a communication throughput of 4 GB/s, 30 GB/s, 80GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links d/M-445, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440-443. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 433 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects 430-431,respectively, and each GPU 410-413 is communicatively coupled to GPUmemory 420-423 over GPU memory interconnects 450-453, respectively. Thememory interconnects 430-431 and 450-453 may utilize the same ordifferent memory access technologies. By way of example, and notlimitation, the processor memories 401-402 and GPU memories 420-423 maybe volatile memories such as dynamic random access memories (DRAMs)(including stacked DRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5,GDDR6), or High Bandwidth Memory (HBM) and/or may be non-volatilememories such as 3D XPoint or Nano-Ram. In one embodiment, some portionof the memories may be volatile memory and another portion may benon-volatile memory (e.g., using a two-level memory (2LM) hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 426may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402.

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneimplementation, a cache 438 stores commands and data for efficientaccess by the graphics processing engines 431-432, N. In one embodiment,the data stored in cache 438 and graphics memories 433-434, N is keptcoherent with the core caches 462A-462D, 456 and system memory 411. Asmentioned, this may be accomplished via proxy circuit 425 which takespart in the cache coherency mechanism on behalf of cache 438 andmemories 433-434, N (e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over link 440, biasingtechniques are used to ensure that the data stored in graphics memories433-434, M is data which will be used most frequently by the graphicsprocessing engines 431-432, N and preferably not used by the cores460A-460D (at least not frequently). Similarly, the biasing mechanismattempts to keep data needed by the cores (and preferably not thegraphics processing engines 431-432, N) within the caches 462A-462D, 456of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherency bus 462 and caches462A-462D, 426.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 446 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engines 431-432, N.It contains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 490 registers 445.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 405and GPU 410 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 405 and viceversa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment, a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2A, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1 .The various parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2A) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 2D) may be configured to perform thefunctions of one or more of a vertex processing unit 504, a tessellationcontrol processing unit 508, a tessellation evaluation processing unit512, a geometry processing unit 516, and a fragment/pixel processingunit 524. The functions of data assembler 502, primitive assemblers 506,514, 518, tessellation unit 510, rasterizer 522, and raster operationsunit 526 may also be performed by other processing engines within aprocessing cluster (e.g., processing cluster 214 of FIG. 3A) and acorresponding partition unit (e.g., partition unit 220A-220N of FIG.2C). The graphics processing pipeline 500 may also be implemented usingdedicated processing units for one or more functions. In one embodiment,one or more portions of the graphics processing pipeline 500 can beperformed by parallel processing logic within a general-purposeprocessor (e.g., CPU). In one embodiment, one or more portions of thegraphics processing pipeline 500 can access on-chip memory (e.g.,parallel processor memory 222 as in FIG. 2A) via a memory interface 528,which may be an instance of the memory interface 218 of FIG. 2A.

In one embodiment, the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinates space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 504. The primitive assembler 506readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 508.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment, the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments, the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and outputs thosefragments and associated coverage data to the fragment/pixel processingunit 524.

The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities, depending on the sampling rate configured forthe processing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z test, blending, andthe like, and outputs pixel data as processed graphics data to bestorage in graphics memory, e.g., parallel processor memory 222 as inFIG. 2A, and/or system memory 104 as in FIG. 1 , to be displayed on theone or more display device(s) 110 or for further processing by one ofthe one or more processor(s) 102 or parallel processor(s) 112. In someembodiments, the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

FIG. 6 illustrates a computing device 600 hosting a topology learningand application mechanism 610 according to one embodiment. Computingdevice 600 represents a communication and data processing deviceincluding (but not limited to) smart wearable devices, smartphones,virtual reality (VR) devices, head-mounted display (HMDs), mobilecomputers, Internet of Things (IoT) devices, laptop computers, desktopcomputers, server computers, etc., and be similar to or the same ascomputing device 100 of FIG. 1 ; accordingly, for brevity, clarity, andease of understanding, many of the details stated above with referenceto FIGS. 1-5 are not further discussed or repeated hereafter.

Computing device 600 may further include (without limitations) anautonomous machine or an artificially intelligent agent, such as amechanical agent or machine, an electronics agent or machine, a virtualagent or machine, an electro-mechanical agent or machine, etc. Examplesof autonomous machines or artificially intelligent agents may include(without limitation) robots, autonomous vehicles (e.g., self-drivingcars, self-flying planes, self-sailing boats, etc.), autonomousequipment (self-operating construction vehicles, self-operating medicalequipment, etc.), and/or the like. Throughout this document, “computingdevice” may be interchangeably referred to as “autonomous machine” or“artificially intelligent agent” or simply “robot”.

It contemplated that although “autonomous vehicle” and “autonomousdriving” are referenced throughout this document, embodiments are notlimited as such. For example, “autonomous vehicle” is not limed to anautomobile but that it may include any number and type of autonomousmachines, such as robots, autonomous equipment, household autonomousdevices, and/or the like, and any one or more tasks or operationsrelating to such autonomous machines may be interchangeably referencedwith autonomous driving.

Computing device 600 may further include (without limitations) largecomputing systems, such as server computers, desktop computers, etc.,and may further include set-top boxes (e.g., Internet-based cabletelevision set-top boxes, etc.), global positioning system (GPS)-baseddevices, etc. Computing device 600 may include mobile computing devicesserving as communication devices, such as cellular phones includingsmartphones, personal digital assistants (PDAs), tablet computers,laptop computers, e-readers, smart televisions, television platforms,wearable devices (e.g., glasses, watches, bracelets, smartcards,jewelry, clothing items, etc.), media players, etc. For example, in oneembodiment, computing device 600 may include a mobile computing deviceemploying a computer platform hosting an integrated circuit (“IC”), suchas system on a chip (“SoC” or “SOC”), integrating various hardwareand/or software components of computing device 600 on a single chip.

As illustrated, in one embodiment, computing device 600 may include anynumber and type of hardware and/or software components, such as (withoutlimitation) graphics processing unit (“GPU” or simply “graphicsprocessor”) 614, graphics driver (also referred to as “GPU driver”,“graphics driver logic”, “driver logic”, user-mode driver (UMD), UMD,user-mode driver framework (UMDF), UMDF, or simply “driver”) 616,central processing unit (“CPU” or simply “application processor”) 612,memory 608, network devices, drivers, or the like, as well asinput/output (I/O) sources 604, such as touchscreens, touch panels,touch pads, virtual or regular keyboards, virtual or regular mice,ports, connectors, etc. Computing device 600 may include operatingsystem (OS) 606 serving as an interface between hardware and/or physicalresources of the computer device 600 and a user. It is contemplated thatgraphics processor 614 and application processor 612 may be one or moreof processor(s) 102 of FIG. 1 .

It is to be appreciated that a lesser or more equipped system than theexample described above may be preferred for certain implementations.Therefore, the configuration of computing device 600 may vary fromimplementation to implementation depending upon numerous factors, suchas price constraints, performance requirements, technologicalimprovements, or other circumstances.

Embodiments may be implemented as any or a combination of: one or moremicrochips or integrated circuits interconnected using a parentboard,hardwired logic, software stored by a memory device and executed by amicroprocessor, firmware, an application specific integrated circuit(ASIC), and/or a field programmable gate array (FPGA). The terms“logic”, “module”, “component”, “engine”, and “mechanism” may include,by way of example, software or hardware and/or combinations of softwareand hardware.

In one embodiment, topology mechanism 610 may be hosted or facilitatedby operating system 606 of computing device 600. In another embodiment,topology mechanism 610 may be hosted by or part of graphics processingunit (“GPU” or simply graphics processor”) 614 or firmware of graphicsprocessor 614. For example, topology mechanism 610 may be embedded in orimplemented as part of the processing hardware of graphics processor614. Similarly, in yet another embodiment, topology mechanism 610 may behosted by or part of central processing unit (“CPU” or simply“application processor”) 612. For example, topology mechanism 610 may beembedded in or implemented as part of the processing hardware ofapplication processor 612. In yet another embodiment, topology mechanism610 may be hosted by or part of any number and type of components ofcomputing device 600, such as a portion of topology mechanism 610 may behosted by or part of operating system 606, another portion may be hostedby or part of graphics processor 614, another portion may be hosted byor part of application processor 612, while one or more portions oftopology mechanism 610 may be hosted by or part of operating system 606and/or any number and type of devices of computing device 600. It iscontemplated that one or more portions or components of topologymechanism 610 may be employed as hardware, software, and/or firmware.

It is contemplated that embodiments are not limited to any particularimplementation or hosting of topology mechanism 610 and that topologymechanism 610 and one or more of its components may be implemented ashardware, software, firmware, or any combination thereof.

Computing device 600 may host network interface(s) to provide access toa network, such as a LAN, a wide area network (WAN), a metropolitan areanetwork (MAN), a personal area network (PAN), Bluetooth, a cloudnetwork, a mobile network (e.g., 3^(rd) Generation (3G), 4^(th)Generation (4G), etc.), an intranet, the Internet, etc. Networkinterface(s) may include, for example, a wireless network interfacehaving antenna, which may represent one or more antenna(e). Networkinterface(s) may also include, for example, a wired network interface tocommunicate with remote devices via network cable, which may be, forexample, an Ethernet cable, a coaxial cable, a fiber optic cable, aserial cable, or a parallel cable.

Embodiments may be provided, for example, as a computer program productwhich may include one or more machine-readable media having storedthereon machine-executable instructions that, when executed by one ormore machines such as a computer, network of computers, or otherelectronic devices, may result in the one or more machines carrying outoperations in accordance with embodiments described herein. Amachine-readable medium may include, but is not limited to, floppydiskettes, optical disks, CD-ROMs (Compact Disc-Read Only Memories), andmagneto-optical disks, ROMs, RAMs, EPROMs (Erasable Programmable ReadOnly Memories), EEPROMs (Electrically Erasable Programmable Read OnlyMemories), magnetic or optical cards, flash memory, or other type ofmedia/machine-readable medium suitable for storing machine-executableinstructions.

Moreover, embodiments may be downloaded as a computer program product,wherein the program may be transferred from a remote computer (e.g., aserver) to a requesting computer (e.g., a client) by way of one or moredata signals embodied in and/or modulated by a carrier wave or otherpropagation medium via a communication link (e.g., a modem and/ornetwork connection).

Throughout the document, term “user” may be interchangeably referred toas “viewer”, “observer”, “person”, “individual”, “end-user”, and/or thelike. It is to be noted that throughout this document, terms like“graphics domain” may be referenced interchangeably with “graphicsprocessing unit”, “graphics processor”, or simply “GPU” and similarly,“CPU domain” or “host domain” may be referenced interchangeably with“computer processing unit”, “application processor”, or simply “CPU”.

It is to be noted that terms like “node”, “computing node”, “server”,“server device”, “cloud computer”, “cloud server”, “cloud servercomputer”, “machine”, “host machine”, “device”, “computing device”,“computer”, “computing system”, and the like, may be usedinterchangeably throughout this document. It is to be further noted thatterms like “application”, “software application”, “program”, “softwareprogram”, “package”, “software package”, and the like, may be usedinterchangeably throughout this document. Also, terms like “job”,“input”, “request”, “message”, and the like, may be used interchangeablythroughout this document.

FIG. 7 illustrates topology learning and application mechanism 610 ofFIG. 6 according to one embodiment. For brevity, many of the detailsalready discussed with reference to FIGS. 1-6 are not repeated ordiscussed hereafter. In one embodiment, topology mechanism 610 mayinclude any number and type of components, such as (withoutlimitations): detection/observation logic 701; generative model logic703; discriminative model logic 705; communication/compatibility logic707; dropout logic 709; decomposition logic 711; on-the-flylearning/updating logic 713; structure learning logic 715; and trainingand feature logic 717.

In one embodiment, detection/observation logic 701 may be used tocontinuously, periodically, or on-demand detect and observe any numberand type of networks and/or models associated with machine learning atautonomous machine 600. For example, in one embodiment,detection/observation logic 701 may be used to detect a deep LVM, M, forlearning purposes, wherein LVM is regarded as a generative probabilisticmodel.

In one embodiment, generative model logic 703 may be used for generatinga structure of a recursive generative model (RGM) by learning a deeplatent variable model, serving as a generative probabilistic model, andusing a novel method (RGM algorithm) as shown in FIG. 8 , where M=RGM(n,G_(start) (V_(start), E_(start)), G_(ex→start)(V_(ex) U V_(start),E_(ex→start)), G_(all)). At first, exit condition provides for all nodesin V_(start) having fewer than n+1 potential parents and returningM={V_(start)} (an observed layer), while d-separation resolution isincreased to n such that thinning the link E_(ex→start) (e.g.,G_(ex→start)) to having a d-separation resolution of n and directingG_(start), and thinning G start to have a d-separation resolution of nand direct its edges.

The process of generative the structure of a recursive generative model(“generative model”), as facilitated by generative model logic 703,continues with identifying of autonomous sub-structures by grouping thenodes having the lowest topological order into a descendantsub-structure, G_(D), where G_(D) is then temporarily removed fromG_(start), while the resulting unconnected structures are defined asancestor sub-structures, G_(A1) . . . G_(Ak). Further, the processcontinues with learning of a generative model structure for eachancestor sub-structure, such as for I−1 to k, call M_(Ai)=RGM(n+1,G_(Ai), G_(ex), G_(all)), and learning of a generative model structurefor the descendant sub-structure, such as call M_(D)=RGM(n+1, G_(D),G_(EXD), G_(all)), where G_(exD)={G_(AI), . . . , G_(Ak), G_(ex)} is theexogenous set to G_(D). Finally, a latent layer is added the structuresare merged, such as for i=1 to k, create a latent layer H_(i), andconnect the inputs of M_(Ai) and M_(D) to its output, where the mergedstructure (e.g., merged top layer L^(n)=U^(k) _(i=1) L_(i) byconcatenation.

In on embodiment, once the generative model is generated by generativemodel logic 703, it may then be converted into a discriminative deepneural network (“discriminative model”) through one or more processes asfacilitated by one or more of discriminative model logic 705. Forexample, discriminative model logic 705 may be triggered to initiate theprocess of generating the discriminative model of out the generativemodel by constructing an inverse graphical model based on the generativemodel. In one embodiment, as illustrated with reference to FIG. 9A, thisinverse graphical model is smartly generated such that any and allconditional dependencies associated with the generative model arepreserved. As further illustrated with reference to FIG. 9A, from asingle generative model, multiple inverse models or simply multipleportions of a single inverse model may be obtained, serving as multipleoptions of existence of a dense network.

In one embodiment, discriminative model may refer to or include adiscriminative probabilistic graphical model and possibly acorresponding deep neural network (where both are discriminativemodels). Overall, the flow may go as follows: learn the structure of agenerative probabilistic model, find its stochastic inverse (which isanother probabilistic graphical model), then convert the stochasticinverse into a discriminative model (graphical model), and finally,convert the discriminative probabilistic graphical model into a deepneural network which is trained using labeled data.

In one embodiment, as illustrated with respect to FIG. 9B, thesemultiple inverse models may then be reduced in number by smartly linkedtogether using bi-directional connections or edges as supplied bydiscriminative model logic 705. For example, bi-directional edges may beused to connect or link latent variables between each pair of latentvariables having a common parent within the inverse model, such aslatent variables that are dependent on one or another where thisdependency may not be modeled by the common parent. In one embodiment,this novel and smart inverse model is obtained by reversing all theedges and adding bi-directional edges between latent variables. Further,this novel inverse model preserves all conditional dependencies that arepresent in the generative model (which, as described above, is thestarting model).

As illustrated with reference to FIG. 9C, in one embodiment,discriminative model logic 705 may be used to generate a final form ofthe discriminative model from the inverse model by applying or adding aclass node, serving as a child of the latent leaves, and removing alland any bi-directional edges. In one embodiment, the added child nodemay include a labeled child node (such as through supervised training)that is then used to provide explanation of any dependencies betweenvarious latent variables. For example, latent nodes may be replaced by aset of neurons and each edge may then be treated as representingdeterministic relation. Using the example above, conditionalprobability, such as P(C=c|X1, X2, X3), may be represented by adeterministic function, such as score(c)=g(W1*ƒ(W2*X+b)+c), where f andg are non-linear functions and W1 and W2 are weight matrices connectinglatent variables to a class node and an observed node, such as (X=[X1,X2, X3]), to the latent variables, respectively.

In one embodiment, an existing network may be used as a starting pointsuch that a first few layers may be maintained and the last few layersmay be removed. A topology may then be learned from the output of thefirst few layers of the starting topology. Further, in anotherembodiment, a network may be learned through raw data or featuresextracted from other raw data, like scale-invariant feature transform(SIFT), as in matching local regions, and histogram of orientalgradients (HOG), used in sliding window fashion, in computer vision,mel-frequency cepstral coefficient (MFCC) in audio, etc.

As described above, this novel technique, as facilitated by topologymechanism 610, is used for learning deep LVMs, serving as probabilisticmodels, and converting these LVMs into DNNs, bridging two machinelearning domains, such as probabilistic graphical models and neuralnetworks, which allows for efficient and systematic coping with largescale problems.

In one embodiment, by constructing a neural network using this noveltechnique, autonomous and/or independent parts of the network (such assome neurons and links between them) may be identified, making itpossible to set a bit-precision of weights in those parts independentlyfrom other parts. For example, in one embodiment, one independent partof the weights may be represented by 1 bit (binary weights) and inanother independent part, it may be represented by 8 bits. Eachindependent part of the neural network may be regarded as a collectionof neurons and links that correspond to the latent node H_i, M_Ai, andM_D in FIG. 8 , block 811, of the generative model, where each latentH_i is replaced by a set of neurons.

Embodiments provide for a methodological dropout, such as a trainingtrick where neurons are randomly dropped each training iteration, asfacilitated by dropout logic 709. For example, using dropout logic 709,this novel technique provides for learning a dropout schedule fromstatistics of any given data, where sets of neurons are dropped jointlyand in a timely fashion. This novel technique for methodological dropoutis smart and in contrast with conventional and commonly-used randomdropout. This methodological dropout allows for knowing in advance whichneurons are to be dropped and capable of being facilitated for higherefficiency by the relevant hardware. Further, a neural network that istrained in using this novel technique of methodological dropout is mostlikely to have a higher or different accuracy than those neural networksthat are trained using a common dropout technique.

Embodiments provide for using decomposition logic 711 to facilitate anovel technique for deep neural network decomposition for generatingparallel and sequential execution schedule and memory sharing withpotentially per-sub-network (learnable) precision level. For example,this novel decomposition technique provides for decomposing a given deepnetwork into a set of smaller independent networks with a schedule forexecution and memory sharing. This novel decomposition technique furtherprovides for a good approximation to the initial network and provides anetwork that runs rather efficiently on a dedicated hardware, whichmakes the technique appropriate for both training and scoring. It iscontemplated that each network may work at a learned precision (such asby weights and activations) given the data it needs to process and thelocal function it needs to approximate.

Embodiments provide for on-the-fly learning/updating logic 713 tofacilitate on-the-fly structure learning and update. For example, thisnovel on-the-fly technique allows the topology of a neural network to beupdated on-the-fly in, for example, certain cases, such as (withoutlimitation): 1) when more data becomes available over time; and 2) thestatistics of the data changes over time. This novel technique furtherprovides for modification of a network at various granularity levels,starting from local changes to rough global changes according to thestatistical power in the data.

Embodiments provide for structure learning logic 715 to facilitateresource inference (anytime) through structure learning. For example, inone embodiment, using structure learning logic 715, a deep networkhaving binary stochastic neurons may be generated that is then able togenerate outputs (such as inference) with accuracy that dynamicallyincreases dynamically over time (and anytime), as necessitated. In otherwords, binary calculations may be performed and aggregated over time, asfacilitated by structure learning logic 715, and this way, if lowaccuracy is needed, less time/compute is spent; for example, wheninferring torque levels applied to robotic arm joints in order to moveit either accurately (such as when placing an object) or roughly (suchas when approaching an object). Another example would be that of a carnavigating without nearby cars or pedestrians (needed low accuracy) asopposed to navigating in a crowded city (needed high accuracy). Thereare not conventional techniques to dynamically update inference andimprove accuracy/precision (anytime inference). This novel structurelearning technique provides for a solution with an ability to graduallyand smoothly change inference accuracy with respect to time/computespent.

In one embodiment, structure learning logic 715 may be further used tofacilitate end-to-end structure learning, such as given a trainingdataset, learning a structure according to the algorithm. Further,structure learning logic 715 facilitates sub-network structure learning,where given a pre-trained initial neural network, the deepest layers inneural networks are replaced with a network-structure that is learned bythe aforementioned algorithm and as facilitated by structure learninglogic 715. This is obtained by feeding the training data through thefirst layers of the initial network and recording their outputs andsubsequently, using this output as an input of training data for thestructure-learning algorithm.

Further, this input of the learned structure may then be connected tothe output of the first layers of the initial network (such as replacingthe deepest layers of the initial network), followed by training of theweights of the entire network by either: 1) fixing the weights of thepre-trained network and learning only the weights of the learnedstructure, and then optionally learning the weights of the entirenetwork; and 2) learning the weights of the entire network from scratch.

Embodiments further provide for training and feature logic 717 forhardware-specific deep network architecture learning for given taskconstraints. For example, in one embodiment, this novel training/featuretechnique facilitates generating a deep network that meets specificarchitectural constrains driven by hardware characteristics (e.g., cachesize, parallelism, precision, etc.) and task requirements (e.g., framerate, latency, and accuracy). This novel technique may further allow forgeneration of a range of networks so that the user may be allowed tochoose a network that performs optimally at a specific working point.There are not conventional techniques that can automatically generate anetwork given hardware and task constraints. This novel techniqueprovides for this functionality of automatically generating a networkthrough a user-interactive tool.

In one embodiment, training and feature logic 717 may be used to copewith large scale data, such as ImageNet. For example, training andfeature logic 717 may be used in large training sets, where a trainingset may be divided into n even sets and the topology of each set is thenlearned and collectively all topologies of all sets are treated as anensemble. Final classification results are obtained by majority votethrough score averaging or even by using a neural network (e.g.,multilayer perception (MLP)) connecting the last layers of the networksand the final class node or other classification method (e.g., supportvector machine (SVM, random forest).

Similarly, in one embodiment, training and feature logic 717 may be usedto facilitate feature bagging that can then be used to reducedimensionality. For example, feature space may be divided into m smallfeature sets, either arbitrarily or using a clustering method (such ask-means). Each set of features may then be used to learn a topology forthat network. The last layers of the topologies are connected to theclass node using a neural network (e.g., MLP) or other classificationmethods (e.g., SMV, random forest).

Further, communication/compatibility logic 707 may be used to facilitatethe needed communication and compatibility between any number of devicesof computing device 600 and various components of topology mechanism610.

Communication/compatibility logic 707 may be used to facilitate dynamiccommunication and compatibility between computing device 600 and anynumber and type of other computing devices (such as mobile computingdevice, desktop computer, server computing device, etc.); processingdevices or components (such as CPUs, GPUs, etc.);capturing/sensing/detecting devices (such as capturing/sensingcomponents including cameras, depth sensing cameras, camera sensors, redgreen blue (“RGB” or “rgb”) sensors, microphones, etc.); display devices(such as output components including display screens, display areas,display projectors, etc.); user/context-awareness components and/oridentification/verification sensors/devices (such as biometricsensors/detectors, scanners, etc.); database(s) 730, such as memory orstorage devices, databases, and/or data sources (such as data storagedevices, hard drives, solid-state drives, hard disks, memory cards ordevices, memory circuits, etc.); communication medium(s) 725, such asone or more communication channels or networks (e.g., cloud networks,the Internet, intranets, cellular networks, proximity networks, such asBluetooth, Bluetooth low energy (BLE), Bluetooth Smart, Wi-Fi proximity,Radio Frequency Identification (RFID), Near Field Communication (NFC),Body Area Network (BAN), etc.); wireless or wired communications andrelevant protocols (e.g., Wi-Fi®, WiMAX, Ethernet, etc.); connectivityand location management techniques; software applications/web sites(e.g., social and/or business networking web sites, etc., businessapplications, games and other entertainment applications, etc.); andprogramming languages, etc., while ensuring compatibility with changingtechnologies, parameters, protocols, standards, etc.

Further, any use of a particular brand, word, term, phrase, name, and/oracronym, such as “detecting”, “observing”, “generating”, “inversing”,“bi-direction edging”, “generative model”, “discriminative model”,“training”, “learning”, “topology”, “structuring”, “feature bagging”,“training set”, “autonomous machine”, “agent”, “machine”, “vehicle”,“robot”, “driving”, “neural network”, “CNN”, “DNN”, “NN”, “executionunit”, “EU”, “shared local memory”, “SLM”, “graphics streams”, “cache”,“graphics cache”, “GPU”, “graphics processor”, “GPU domain”, “GPGPU”,“CPU”, “application processor”, “CPU domain”, “graphics driver”,“workload”, “application”, “graphics pipeline”, “pipeline processes”,“API”, “3D API”, “OpenGL®”, “DirectX®”, “hardware”, “software”, “agent”,“graphics driver”, “kernel mode graphics driver”, “user-mode driver”,“user-mode driver framework”, “buffer”, “graphics buffer”, “task”,“process”, “operation”, “software application”, “game”, etc., should notbe read to limit embodiments to software or devices that carry thatlabel in products or in literature external to this document.

It is contemplated that any number and type of components may be addedto and/or removed from topology mechanism 610 to facilitate variousembodiments including adding, removing, and/or enhancing certainfeatures. For brevity, clarity, and ease of understanding of topologymechanism 610, many of the standard and/or known components, such asthose of a computing device, are not shown or discussed here. It iscontemplated that embodiments, as described herein, are not limited toany particular technology, topology, system, architecture, and/orstandard and are dynamic enough to adopt and adapt to any futurechanges.

FIG. 8 illustrates a recursive deep generative model according to oneembodiment. For brevity, many of the details previously discussed withreference to FIGS. 1-7 may not be discussed or repeated hereafter. Anyprocesses relating to method 800 may be performed by processing logicthat may comprise hardware (e.g., circuitry, dedicated logic,programmable logic, etc.), software (such as instructions run on aprocessing device), or a combination thereof, as facilitated by topologymechanism 610 of FIG. 6 . The processes associated with method 800 maybe illustrated or recited in linear sequences for brevity and clarity inpresentation; however, it is contemplated that any number of them can beperformed in parallel, asynchronously, or in different orders.

Method 800 to generate a recursive generative model in machine learningat an autonomous machine begins at block 801 with an existing conditionand continues on at block 803 with increasing of d-separation resolutionto n. At block 805, autonomous sub-structures are identified and atblock 807, a generative model structure for each ancestor sub-structureis learned. At block 809, a generative model structure for descendentsub-structures is learned and finally, at block 811, a latent layer isadded, while the sub-structures are merged into a single structurerepresenting the recursive generative model.

FIG. 9A illustrates inverse graphical models 903, 905 according to oneembodiment. For brevity, many of the details previously discussed withreference to FIGS. 1-8 may not be discussed or repeated hereafter. It iscontemplated that the embodiments are not limited to the illustrationand that any number of models and sub-models may be generated andinvolved in real-life machine learning.

In the illustrated embodiment and as further discussed with reference toFIG. 7 , in one embodiment, a generative model, such as generative model901, may be converted into to a discriminative model, such asdiscriminative model 921 of FIG. 9C, where this conversion processbegins with constructing inverse graphical models 903, 905 fromgenerative model 901. As illustrated, this conversion produces multipleinverse models 903, 905, where X1, X2, and X3 are now parent nodes,while H1 and H2 are child nodes (which is the reverse or inverse ofgenerative model 901).

FIG. 9B illustrates an inverse model 911 having a bi-directionconnection according to one embodiment. For brevity, many of the detailspreviously discussed with reference to FIGS. 1-9A may not be discussedor repeated hereafter. It is contemplated that the embodiments are notlimited to the illustration and that any number of models and sub-modelsmay be generated and involved in real-life machine learning.

As discussed with respect to FIG. 9A, multiple inverse models 903, 905were obtained by reversing generative model 901 and thus, in oneembodiment, inverse model 903 of the two inverse models 903, 905 of FIG.9A is selected and to which, a bi-directional connection 913 is added toconvert inverse model 903 into a bi-directional inverse model 911. Forexample, in the illustrated embodiment, bi-directional edges representedby bi-direction arrow/connection 913 are assigned to connect any latentvariables between each pair of latent variables having a common parent.This novel technique allows for latent variables to maintain theirdependencies, while consolidating or combining multiple inverse models903, 905 of FIG. 9A into a single inverse bi-directional model 911.

FIG. 9C illustrates a discriminative model 921 according to oneembodiment. For brevity, many of the details previously discussed withreference to FIGS. 1-9B may not be discussed or repeated hereafter. Itis contemplated that the embodiments are not limited to the illustrationand that any number of models and sub-models may be generated andinvolved in real-life machine learning.

In the illustrated embodiment, discriminative model 921 is obtained byadding class node 923 as a child node of the two latent leaves, such asH1 and H2, through connections 925, 927. Further, in one embodiment uponadding class node 923, bi-directional connection 913 of FIG. 9B isremoved, which results in this discriminative model 921.

FIG. 9D illustrates another embodiment of graphical models 930, 931,935, 939 according to one embodiment. As previously illustrated anddescribed with reference to FIGS. 9A-9C, generative model 930, graph L,may be converted into inverse model 931 based on single direction 933.In one embodiment, inverse model 931 is then converted intobi-directional inverse model 935 having employed bi-directionalconnection 937, which is then further converted into discriminativemodel 939 having added class node 941.

FIG. 9E illustrates another embodiment of graphical models 951, 953according to one embodiment. As previously illustrated and describedwith reference to FIGS. 9A-9D, illustrated here is model 951representing an essential graphic, encoding only marginal independenciesand identified autonomous substructures, and having dependencies whereboth nodes A and B are in coupled to all other nodes, C, E and D. Thismay then be converted into a simplified version as shown in model 953,where nodes A and B are still coupled to all three nodes C, E, and D.

FIG. 9F illustrates another embodiment of graphical models 955, 957,961, 963 according to one embodiment. As previously illustrated anddescribed with reference to FIGS. 9A-9E, illustrated here is generativemodel 955 and a more complex form of model 955 is shown as model 957. Inone embodiment, model 955 may then be modified as bi-directional model961 having multiple bi-directional connections 963 and 965 andsubsequently, discriminative model 965 is generated by add class node967 and removing bi-directional connections 963 and 965.

Machine Learning Overview

A machine learning algorithm is an algorithm that can learn based on aset of data. Embodiments of machine learning algorithms can be designedto model high-level abstractions within a data set. For example, imagerecognition algorithms can be used to determine which of severalcategories to which a given input belong; regression algorithms canoutput a numerical value given an input; and pattern recognitionalgorithms can be used to generate translated text or perform text tospeech and/or speech recognition.

An exemplary type of machine learning algorithm is a neural network.There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 10 is a generalized diagram of a machine learning software stack1000. A machine learning application 1002 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 1002 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 1002can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 1002 can beenabled via a machine learning framework 1004. The machine learningframework 1004 can provide a library of machine learning primitives.Machine learning primitives are basic operations that are commonlyperformed by machine learning algorithms. Without the machine learningframework 1004, developers of machine learning algorithms would berequired to create and optimize the main computational logic associatedwith the machine learning algorithm, then re-optimize the computationallogic as new parallel processors are developed. Instead, the machinelearning application can be configured to perform the necessarycomputations using the primitives provided by the machine learningframework 1004. Exemplary primitives include tensor convolutions,activation functions, and pooling, which are computational operationsthat are performed while training a convolutional neural network (CNN).The machine learning framework 1004 can also provide primitives toimplement basic linear algebra subprograms performed by manymachine-learning algorithms, such as matrix and vector operations.

The machine learning framework 1004 can process input data received fromthe machine learning application 1002 and generate the appropriate inputto a compute framework 1006. The compute framework 1006 can abstract theunderlying instructions provided to the GPGPU driver 1008 to enable themachine learning framework 1004 to take advantage of hardwareacceleration via the GPGPU hardware 1010 without requiring the machinelearning framework 1004 to have intimate knowledge of the architectureof the GPGPU hardware 1010. Additionally, the compute framework 1006 canenable hardware acceleration for the machine learning framework 1004across a variety of types and generations of the GPGPU hardware 1010.

GPGPU Machine Learning Acceleration

FIG. 11 illustrates a highly-parallel general-purpose graphicsprocessing unit 1100, according to an embodiment. In one embodiment, thegeneral-purpose processing unit (GPGPU) 1100 can be configured to beparticularly efficient in processing the type of computational workloadsassociated with training deep neural networks. Additionally, the GPGPU1100 can be linked directly to other instances of the GPGPU to create amulti-GPU cluster to improve training speed for particularly deep neuralnetworks.

The GPGPU 1100 includes a host interface 1102 to enable a connectionwith a host processor. In one embodiment, the host interface 1102 is aPCI Express interface. However, the host interface can also be a vendorspecific communications interface or communications fabric. The GPGPU1100 receives commands from the host processor and uses a globalscheduler 1104 to distribute execution threads associated with thosecommands to a set of compute clusters 1106A-H. The compute clusters1106A-H share a cache memory 1108. The cache memory 1108 can serve as ahigher-level cache for cache memories within the compute clusters1106A-H.

The GPGPU 1100 includes memory 1114A-B coupled with the compute clusters1106A-H via a set of memory controllers 1112A-B. In various embodiments,the memory 1114A-B can include various types of memory devices includingdynamic random access memory (DRAM) or graphics random access memory,such as synchronous graphics random access memory (SGRAM), includinggraphics double data rate (GDDR) memory. In one embodiment, the memoryunits 224A-N may also include 3D stacked memory, including but notlimited to high bandwidth memory (HBM). In one embodiment, each computecluster GPLAB06A-H includes a set of graphics multiprocessors, such asthe graphics multiprocessor 400 of FIG. 4A. The graphics multiprocessorsof the compute cluster multiple types of integer and floating pointlogic units that can perform computational operations at a range ofprecisions including suited for machine learning computations. Forexample, and in one embodiment at least a subset of the floating-pointunits in each of the compute clusters 1106A-H can be configured toperform 16-bit or 32-bit floating point operations, while a differentsubset of the floating-point units can be configured to perform 64-bitfloating point operations.

Multiple instances of the GPGPU 1100 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment, the multiple instances of the GPGPU 1100 communicate overthe host interface 1102. In one embodiment. the GPGPU 1100 includes anI/O hub 1108 that couples the GPGPU 1100 with a GPU link 1110 thatenables a direct connection to other instances of the GPGPU. In oneembodiment, the GPU link 1110 is coupled to a dedicated GPU-to-GPUbridge that enables communication and synchronization between multipleinstances of the GPGPU 1100. In one embodiment, the GPU link 1110couples with a high-speed interconnect to transmit and receive data toother GPGPUs or parallel processors. In one embodiment, the multipleinstances of the GPGPU 1100 are located in separate data processingsystems and communicate via a network device that is accessible via thehost interface 1102. In one embodiment, the GPU link 1110 can beconfigured to enable a connection to a host processor in addition to oras an alternative to the host interface 1102.

While the illustrated configuration of the GPGPU 1100 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 1100 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration, the GPGPU 1100 includes fewer of the computeclusters 1106A-H relative to the training configuration. Additionally,memory technology associated with the memory 1114A-B may differ betweeninferencing and training configurations. In one embodiment, theinferencing configuration of the GPGPU 1100 can support inferencingspecific instructions. For example, an inferencing configuration canprovide support for one or more 8-bit integer dot product instructions,which are commonly used during inferencing operations for deployedneural networks.

FIG. 12 illustrates a multi-GPU computing system 1200, according to anembodiment. The multi-GPU computing system 1200 can include a processor1202 coupled to multiple GPGPUs 1206A-D via a host interface switch1204. The host interface switch 1204, in one embodiment, is a PCIexpress switch device that couples the processor 1202 to a PCI expressbus over which the processor 1202 can communicate with the set of GPGPUs1206A-D. Each of the multiple GPGPUs 1206A-D can be an instance of theGPGPU 1100 of FIG. 11 . The GPGPUs 1206A-D can interconnect via a set ofhigh-speed points to point GPU to GPU links 1216. The high-speed GPU toGPU links can connect to each of the GPGPUs 1206A-D via a dedicated GPUlink, such as the GPU link 1110 as in FIG. 11 . The P2P GPU links 1216enable direct communication between each of the GPGPUs 1206A-D withoutrequiring communication over the host interface bus to which theprocessor 1202 is connected. With GPU-to-GPU traffic directed to the P2PGPU links, the host interface bus remains available for system memoryaccess or to communicate with other instances of the multi-GPU computingsystem 1200, for example, via one or more network devices. While in theillustrated embodiment the GPGPUs 1206A-D connect to the processor 1202via the host interface switch 1204, in one embodiment the processor 1202includes direct support for the P2P GPU links 1216 and can connectdirectly to the GPGPUs 1206A-D.

Machine Learning Neural Network Implementations

The computing architecture provided by embodiments described herein canbe configured to perform the types of parallel processing that isparticularly suited for training and deploying neural networks formachine learning. A neural network can be generalized as a network offunctions having a graph relationship. As is well-known in the art,there are a variety of types of neural network implementations used inmachine learning. One exemplary type of neural network is thefeedforward network, as previously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for compute vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting as toany specific embodiment described herein and the concepts illustratedcan be applied generally to deep neural networks and machine learningtechniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIG. 13A-B illustrate an exemplary convolutional neural network. FIG.13A illustrates various layers within a CNN. As shown in FIG. 13A, anexemplary CNN used to model image processing can receive input 1302describing the red, green, and blue (RGB) components of an input image.The input 1302 can be processed by multiple convolutional layers (e.g.,convolutional layer 1304, convolutional layer 1306). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 1308. Neurons in a fully connected layer havefull connections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 1308 can be used to generate an output result from the network.The activations within the fully connected layers 1308 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations are make use of fully connected layers DPLA08. Forexample, in some implementations the convolutional layer 1306 cangenerate output for the CNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 1308. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 13B illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 1312 of a CNN can beprocessed in three stages of a convolutional layer 1314. The threestages can include a convolution stage 1316, a detector stage 1318, anda pooling stage 1320. The convolution layer 1314 can then output data toa successive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 1316 performs several convolutions in parallelto produce a set of linear activations. The convolution stage 1316 caninclude an affine transformation, which is any transformation that canbe specified as a linear transformation plus a translation. Affinetransformations include rotations, translations, scaling, andcombinations of these transformations. The convolution stage computesthe output of functions (e.g., neurons) that are connected to specificregions in the input, which can be determined as the local regionassociated with the neuron. The neurons compute a dot product betweenthe weights of the neurons and the region in the local input to whichthe neurons are connected. The output from the convolution stage 1316defines a set of linear activations that are processed by successivestages of the convolutional layer 1314.

The linear activations can be processed by a detector stage 1318. In thedetector stage 1318, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asƒ(x)=max (0, x), such that the activation is thresholded at zero.

The pooling stage 1320 uses a pooling function that replaces the outputof the convolutional layer 1306 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 1320,including max pooling, average pooling, and l2-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 1314 can then be processed bythe next layer 1322. The next layer 1322 can be an additionalconvolutional layer or one of the fully connected layers 1308. Forexample, the first convolutional layer 1304 of FIG. 13A can output tothe second convolutional layer 1306, while the second convolutionallayer can output to a first layer of the fully connected layers 1308.

FIG. 14 illustrates an exemplary recurrent neural network 1400. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 1400 can bedescribed has having an input layer 1402 that receives an input vector,hidden layers 1404 to implement a recurrent function, a feedbackmechanism 1405 to enable a ‘memory’ of previous states, and an outputlayer 1406 to output a result. The RNN 1400 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 1405. For agiven time step, the state of the hidden layers 1404 is defined by theprevious state and the input at the current time step. An initial input(x₁) at a first-time step can be processed by the hidden layer 1404. Asecond input (x₂) can be processed by the hidden layer 1404 using stateinformation that is determined during the processing of the initialinput (x₁). A given state can be computed as s_(t)=ƒ(Ux_(t)+Ws_(t-1)),where U and W are parameter matrices. The function ƒ is generally anonlinearity, such as the hyperbolic tangent function (Tan h) or avariant of the rectifier function ƒ(x)=max (0, x). However, the specificmathematical function used in the hidden layers 1404 can vary dependingon the specific implementation details of the RNN 1400.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the long shortterm memory (LSTM) RNN. LSTM RNNs are capable of learning long-termdependencies that may be necessary for processing longer sequences oflanguage. A variant on the CNN is a convolutional deep belief network,which has a structure similar to a CNN and is trained in a mannersimilar to a deep belief network. A deep belief network (DBN) is agenerative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 15 illustrates training and deployment of a deep neural network.Once a given network has been structured for a task the neural networkis trained using a training dataset 1502. Various training frameworks1504 have been developed to enable hardware acceleration of the trainingprocess. For example, the machine learning framework 1004 of FIG. 10 maybe configured as a training framework 1004. The training framework 1004can hook into an untrained neural network 1506 and enable the untrainedneural net to be trained using the parallel processing resourcesdescribed herein to generate a trained neural net 1508.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 1502 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 1504 can adjust to adjust the weights that controlthe untrained neural network 1506. The training framework 1504 canprovide tools to monitor how well the untrained neural network 1506 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural net 1508. The trained neural network 1508 can then bedeployed to implement any number of machine learning operations.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 1502 will include input data without any associatedoutput data. The untrained neural network 1506 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 1507 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset1502 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 1508 to adapt tothe new data 1512 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 16 is a block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes to perform supervised or unsupervised training of aneural network. The distributed computational nodes can each include oneor more host processors and one or more of the general-purposeprocessing nodes, such as the highly-parallel general-purpose graphicsprocessing unit 1100 as in FIG. 1100 . As illustrated, distributedlearning can be performed model parallelism 1602, data parallelism 1604,or a combination of model and data parallelism 1604.

In model parallelism 1602, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks.

In data parallelism 1604, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 1606 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thehighly-parallel general-purpose graphics processing unit 1100 of FIG.1100 and the multi-GPU computing system 1200 of FIG. 1200 . On thecontrary, deployed machine learning platforms generally include lowerpower parallel processors suitable for use in products such as cameras,autonomous robots, and autonomous vehicles.

FIG. 17 illustrates an exemplary inferencing system on a chip (SOC) 1700suitable for performing inferencing using a trained model. The SOC 1700can integrate processing components including a media processor 1702, avision processor 1704, a GPGPU 1706 and a multi-core processor 1708. TheSOC 1700 can additionally include on-chip memory 1705 that can enable ashared on-chip data pool that is accessible by each of the processingcomponents. The processing components can be optimized for low poweroperation to enable deployment to a variety of machine learningplatforms, including autonomous vehicles and autonomous robots. Forexample, one implementation of the SOC 1700 can be used as a portion ofthe main control system for an autonomous vehicle. Where the SOC 1700 isconfigured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 1702 and vision processor 1704 canwork in concert to accelerate computer vision operations. The mediaprocessor 1702 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip-memory 1705. The vision processor 1704 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 1704 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back end model computations areperformed by the GPGPU 1706.

The multi-core processor 1708 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 1702 and the visionprocessor 1704. The multi-core processor 1708 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 1706. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 1708. Such softwarecan directly issue computational workloads to the GPGPU 1706 or thecomputational workloads can be issued to the multi-core processor 1708,which can offload at least a portion of those operations to the GPGPU1706.

The GPGPU 1706 can include compute clusters such as a low powerconfiguration of the compute clusters 1106A-1106H within thehighly-parallel general-purpose graphics processing unit 1100. Thecompute clusters within the GPGPU 1706 can support instruction that arespecifically optimized to perform inferencing computations on a trainedneural network. For example, the GPGPU 1706 can support instructions toperform low precision computations such as 8-bit and 4-bit integervector operations.

System Overview II

FIG. 18 is a block diagram of a processing system 1800, according to anembodiment. In various embodiments, the system 1800 includes one or moreprocessors 1802 and one or more graphics processors 1808, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 1802 or processorcores 1807. In on embodiment, the system 1800 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 1800 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 1800 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 1800 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 1800 is a television or set topbox device having one or more processors 1802 and a graphical interfacegenerated by one or more graphics processors 1808.

In some embodiments, the one or more processors 1802 each include one ormore processor cores 1807 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 1807 is configured to process aspecific instruction set 1809. In some embodiments, instruction set 1809may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 1807 may each processa different instruction set 1809, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 1807may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 1802 includes cache memory 1804.Depending on the architecture, the processor 1802 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 1802. In some embodiments, the processor 1802 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 1807 using knowncache coherency techniques. A register file 1806 is additionallyincluded in processor 1802 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 1802.

In some embodiments, processor 1802 is coupled to a processor bus 1810to transmit communication signals such as address, data, or controlsignals between processor 1802 and other components in system 1800. Inone embodiment, the system 1800 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 1816 and an Input Output(I/O) controller hub 1830. A memory controller hub 1816 facilitatescommunication between a memory device and other components of system1800, while an I/O Controller Hub (ICH) 1830 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 1816 is integrated within the processor.

Memory device 1820 can be a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment, the memorydevice 1820 can operate as system memory for the system 1800, to storedata 1822 and instructions 1821 for use when the one or more processors1802 executes an application or process. Memory controller hub 1816 alsocouples with an optional external graphics processor 1812, which maycommunicate with the one or more graphics processors 1808 in processors1802 to perform graphics and media operations.

In some embodiments, ICH 1830 enables peripherals to connect to memorydevice 1820 and processor 1802 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 1846, afirmware interface 1828, a wireless transceiver 1826 (e.g., Wi-Fi,Bluetooth), a data storage device 1824 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 1840 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 1842 connect input devices, suchas keyboard and mouse 1844 combinations. A network controller 1834 mayalso couple to ICH 1830. In some embodiments, a high-performance networkcontroller (not shown) couples to processor bus 1810. It will beappreciated that the system 1800 shown is exemplary and not limiting, asother types of data processing systems that are differently configuredmay also be used. For example, the I/O controller hub 1830 may beintegrated within the one or more processor 1802, or the memorycontroller hub 1816 and I/O controller hub 1830 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 1812.

FIG. 19 is a block diagram of an embodiment of a processor 1900 havingone or more processor cores 1902A-1902N, an integrated memory controller1914, and an integrated graphics processor 1908. Those elements of FIG.19 having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor1900 can include additional cores up to and including additional core1902N represented by the dashed lined boxes. Each of processor cores1902A-1902N includes one or more internal cache units 1904A-1904N. Insome embodiments, each processor core also has access to one or moreshared cached units 1906.

The internal cache units 1904A-1904N and shared cache units 1906represent a cache memory hierarchy within the processor 1900. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 1906 and1904A-1904N.

In some embodiments, processor 1900 may also include a set of one ormore bus controller units 1916 and a system agent core 1910. The one ormore bus controller units 1916 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 1910 provides management functionality forthe various processor components. In some embodiments, system agent core1910 includes one or more integrated memory controllers 1914 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 1902A-1902Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 1910 includes components for coordinating andoperating cores 1902A-1902N during multi-threaded processing. Systemagent core 1910 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 1902A-1902N and graphics processor 1908.

In some embodiments, processor 1900 additionally includes graphicsprocessor 1908 to execute graphics processing operations. In someembodiments, the graphics processor 1908 couples with the set of sharedcache units 1906, and the system agent core 1910, including the one ormore integrated memory controllers 1914. In some embodiments, a displaycontroller 1911 is coupled with the graphics processor 1908 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 1911 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 1908 or system agent core 1910.

In some embodiments, a ring based interconnect unit 1912 is used tocouple the internal components of the processor 1900. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 1908 couples with the ring interconnect 1912 via an I/O link1913.

The exemplary I/O link 1913 represents at least one of multiplevarieties of I/O interconnects, including an on-package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 1918, such as an eDRAM module.In some embodiments, each of the processor cores 1902-1902N and graphicsprocessor 1908 use embedded memory modules 1918 as a shared Last LevelCache.

In some embodiments, processor cores 1902A-1902N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 1902A-1902N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 1902A-Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 1902A-1902N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor1900 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 20 is a block diagram of a graphics processor 2000, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 2000 includesa memory interface 2014 to access memory. Memory interface 2014 can bean interface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 2000 also includes a displaycontroller 2002 to drive display output data to a display device 2020.Display controller 2002 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 2000includes a video codec engine 2006 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 2000 includes a block imagetransfer (BLIT) engine 2004 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 2010. In someembodiments, graphics processing engine 2010 is a compute engine forperforming graphics operations, including three-dimensional (3D)graphics operations and media operations.

In some embodiments, GPE 2010 includes a 3D pipeline 2012 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 2012 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 2015.While 3D pipeline 2012 can be used to perform media operations, anembodiment of GPE 2010 also includes a media pipeline 2016 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 2016 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 2006. In some embodiments, media pipeline 2016 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 2015. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 2015.

In some embodiments, 3D/Media subsystem 2015 includes logic forexecuting threads spawned by 3D pipeline 2012 and media pipeline 2016.In one embodiment, the pipelines send thread execution requests to3D/Media subsystem 2015, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 2015 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

3D/Media Processing

FIG. 21 is a block diagram of a graphics processing engine 2110 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 2110 is a version ofthe GPE 2010 shown in FIG. 20 . Elements of FIG. 21 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. For example, the 3Dpipeline 2012 and media pipeline 2016 of FIG. 20 are illustrated. Themedia pipeline 2016 is optional in some embodiments of the GPE 2110 andmay not be explicitly included within the GPE 2110. For example, and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 2110.

In some embodiments, GPE 2110 couples with or includes a commandstreamer 2103, which provides a command stream to the 3D pipeline 2012and/or media pipelines 2016. In some embodiments, command streamer 2103is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 2103 receives commands from the memory and sends thecommands to 3D pipeline 2012 and/or media pipeline 2016. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 2012 and media pipeline 2016. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 2012 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 2012 and/or image data andmemory objects for the media pipeline 2016. The 3D pipeline 2012 andmedia pipeline 2016 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 2114.

In various embodiments, the 3D pipeline 2012 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 2114. The graphics core array 2114 provides aunified block of execution resources. Multi-purpose execution logic(e.g., execution units) within the graphic core array 2114 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 2114 also includesexecution logic to perform media functions, such as video and/or imageprocessing. In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general-purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 1807 of FIG. 18 or core 1902A-1902N as in FIG. 19 .

Output data generated by threads executing on the graphics core array2114 can output data to memory in a unified return buffer (URB) 2118.The URB 2118 can store data for multiple threads. In some embodiments,the URB 2118 may be used to send data between different threadsexecuting on the graphics core array 2114. In some embodiments, the URB2118 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 2120.

In some embodiments, graphics core array 2114 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 2110. In one embodiment, the executionresources are dynamically scalable, such that execution resources may beenabled or disabled as needed.

The graphics core array 2114 couples with shared function logic 2120that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 2120 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 2114. In variousembodiments, shared function logic 2120 includes but is not limited tosampler 2121, math 2122, and inter-thread communication (ITC) 2123logic. Additionally, some embodiments implement one or more cache(s)2125 within the shared function logic 2120. A shared function isimplemented where the demand for a given specialized function isinsufficient for inclusion within the graphics core array 2114. Insteada single instantiation of that specialized function is implemented as astand-alone entity in the shared function logic 2120 and shared amongthe execution resources within the graphics core array 2114. The preciseset of functions that are shared between the graphics core array 2114and included within the graphics core array 2114 varies betweenembodiments.

FIG. 22 is a block diagram of another embodiment of a graphics processor2200. Elements of FIG. 22 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 2200 includes a ringinterconnect 2202, a pipeline front-end 2204, a media engine 2237, andgraphics cores 2280A-2280N. In some embodiments, ring interconnect 2202couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 2200 receives batches ofcommands via ring interconnect 2202. The incoming commands areinterpreted by a command streamer 2203 in the pipeline front-end 2204.In some embodiments, graphics processor 2200 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 2280A-2280N. For 3D geometry processing commands,command streamer 2203 supplies commands to geometry pipeline 2236. Forat least some media processing commands, command streamer 2203 suppliesthe commands to a video front end 2234, which couples with a mediaengine 2237. In some embodiments, media engine 2237 includes a VideoQuality Engine (VQE) 2230 for video and image post-processing and amulti-format encode/decode (MFX) 2233 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 2236 and media engine 2237 each generate executionthreads for the thread execution resources provided by at least onegraphics core 2280A.

In some embodiments, graphics processor 2200 includes scalable threadexecution resources featuring modular cores 2280A-2280N (sometimesreferred to as core slices), each having multiple sub-cores 2250A-2250N,2260A-2260N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 2200 can have any number of graphicscores 2280A through 2280N. In some embodiments, graphics processor 2200includes a graphics core 2280A having at least a first sub-core 2250Aand a second core sub-core 2260A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 2250A).In some embodiments, graphics processor 2200 includes multiple graphicscores 2280A-2280N, each including a set of first sub-cores 2250A-2250Nand a set of second sub-cores 2260A-2260N. Each sub-core in the set offirst sub-cores 2250A-2250N includes at least a first set of executionunits 2252A-2252N and media/texture samplers 2254A-2254N. Each sub-corein the set of second sub-cores 2260A-2260N includes at least a secondset of execution units 2262A-2262N and samplers 2264A-2264N. In someembodiments, each sub-core 2250A-2250N, 2260A-2260N shares a set ofshared resources 2270A-2270N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

Execution Logic

FIG. 23 illustrates thread execution logic 2300 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 23 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 2300 includes a pixel shader2302, a thread dispatcher 2304, instruction cache 2306, a scalableexecution unit array including a plurality of execution units2308A-2308N, a sampler 2310, a data cache 2312, and a data port 2314. Inone embodiment, the included components are interconnected via aninterconnect fabric that links to each of the components. In someembodiments, thread execution logic 2300 includes one or moreconnections to memory, such as system memory or cache memory, throughone or more of instruction cache 2306, data port 2314, sampler 2310, andexecution unit array 2308A-2308N. In some embodiments, each executionunit (e.g. 2308A) is an individual vector processor capable of executingmultiple simultaneous threads and processing multiple data elements inparallel for each thread. In some embodiments, execution unit array2308A-2308N includes any number individual execution units.

In some embodiments, execution unit array 2308A-2308N is primarily usedto execute “shader” programs. In some embodiments, the execution unitsin array 2308A-2308N execute an instruction set that includes nativesupport for many standard 3D graphics shader instructions, such thatshader programs from graphics libraries (e.g., Direct 3D and OpenGL) areexecuted with a minimal translation. The execution units support vertexand geometry processing (e.g., vertex programs, geometry programs,vertex shaders), pixel processing (e.g., pixel shaders, fragmentshaders) and general-purpose processing (e.g., compute and mediashaders).

Each execution unit in execution unit array 2308A-2308N operates onarrays of data elements. The number of data elements is the “executionsize,” or the number of channels for the instruction. An executionchannel is a logical unit of execution for data element access, masking,and flow control within instructions. The number of channels may beindependent of the number of physical Arithmetic Logic Units (ALUs) orFloating Point Units (FPUs) for a particular graphics processor. In someembodiments, execution units 2308A-2308N support integer andfloating-point data types.

The execution unit instruction set includes single instruction multipledata (SIMD) or single instruction multiple thread (SIMT) instructions.The various data elements can be stored as a packed data type in aregister and the execution unit will process the various elements basedon the data size of the elements. For example, when operating on a256-bit wide vector, the 256 bits of the vector are stored in a registerand the execution unit operates on the vector as four separate 64-bitpacked data elements (Quad-Word (QW) size data elements), eight separate32-bit packed data elements (Double Word (DW) size data elements),sixteen separate 16-bit packed data elements (Word (W) size dataelements), or thirty-two separate 8-bit data elements (byte (B) sizedata elements). However, different vector widths and register sizes arepossible.

One or more internal instruction caches (e.g., 2306) are included in thethread execution logic 2300 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,2312) are included to cache thread data during thread execution. In someembodiments, sampler 2310 is included to provide texture sampling for 3Doperations and media sampling for media operations. In some embodiments,sampler 2310 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 2300 via thread spawningand dispatch logic. In some embodiments, thread execution logic 2300includes a local thread dispatcher 2304 that arbitrates threadinitiation requests from the graphics and media pipelines andinstantiates the requested threads on one or more execution units2308A-2308N. For example, the geometry pipeline (e.g., 2236 of FIG. 22 )dispatches vertex processing, tessellation, or geometry processingthreads to thread execution logic 2300 (FIG. 23 ). In some embodiments,thread dispatcher 2304 can also process runtime thread spawning requestsfrom the executing shader programs.

Once a group of geometric objects has been processed and rasterized intopixel data, pixel shader 2302 is invoked to further compute outputinformation and cause results to be written to output surfaces (e.g.,color buffers, depth buffers, stencil buffers, etc.). In someembodiments, pixel shader 2302 calculates the values of the variousvertex attributes that are to be interpolated across the rasterizedobject. In some embodiments, pixel shader 2302 then executes anapplication programming interface (API)-supplied pixel shader program.To execute the pixel shader program, pixel shader 2302 dispatchesthreads to an execution unit (e.g., 2308A) via thread dispatcher 2304.In some embodiments, pixel shader 2302 uses texture sampling logic insampler 2310 to access texture data in texture maps stored in memory.Arithmetic operations on the texture data and the input geometry datacompute pixel color data for each geometric fragment, or discards one ormore pixels from further processing.

In some embodiments, the data port 2314 provides a memory accessmechanism for the thread execution logic 2300 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 2314 includes or couples to one or more cachememories (e.g., data cache 2312) to cache data for memory access via thedata port.

FIG. 24 is a block diagram illustrating a graphics processor instructionformats 2400 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 2400 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 2410. A 64-bitcompacted instruction format 2430 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 2410 provides access toall instruction options, while some options and operations arerestricted in the 64-bit instruction format 2430. The nativeinstructions available in the 64-bit instruction format 2430 vary byembodiment. In some embodiments, the instruction is compacted in partusing a set of index values in an index field 2413. The execution unithardware references a set of compaction tables based on the index valuesand uses the compaction table outputs to reconstruct a nativeinstruction in the 128-bit instruction format 2410.

For each format, instruction opcode 2412 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 2414 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). For 128-bitinstructions 2410 an exec-size field 2416 limits the number of datachannels that will be executed in parallel. In some embodiments,exec-size field 2416 is not available for use in the 64-bit compactinstruction format 2430.

Some execution unit instructions have up to three operands including twosource operands, src0 2420, src1 2422, and one destination 2418. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 2424), where the instructionopcode 2412 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 2410 includes anaccess/address mode information 2426 specifying, for example, whetherdirect register addressing mode or indirect register addressing mode isused. When direct register addressing mode is used, the register addressof one or more operands is directly provided by bits in the instruction2410.

In some embodiments, the 128-bit instruction format 2410 includes anaccess/address mode field 2426, which specifies an address mode and/oran access mode for the instruction. In one embodiment, the access modeto define a data access alignment for the instruction. Some embodimentssupport access modes including a 16-byte aligned access mode and a1-byte aligned access mode, where the byte alignment of the access modedetermines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction 2410 may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction 2410 may use 16-byte-aligned addressing for allsource and destination operands.

In one embodiment, the address mode portion of the access/address modefield 2426 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction 2410 directly provide the register address of one ormore operands. When indirect register addressing mode is used, theregister address of one or more operands may be computed based on anaddress register value and an address immediate field in theinstruction.

In some embodiments, instructions are grouped based on opcode 2412bit-fields to simplify Opcode decode 2440. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 2442 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 2442 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 2444 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 2446 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 2448 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 2448 performs the arithmetic operations in parallelacross data channels. The vector math group 2450 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Graphics Pipeline

FIG. 25 is a block diagram of another embodiment of a graphics processor2500. Elements of FIG. 25 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 2500 includes a graphicspipeline 2520, a media pipeline 2530, a display engine 2540, threadexecution logic 2550, and a render output pipeline 2570. In someembodiments, graphics processor 2500 is a graphics processor within amulti-core processing system that includes one or more general-purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 2500 via a ring interconnect 2502. In someembodiments, ring interconnect 2502 couples graphics processor 2500 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 2502 areinterpreted by a command streamer 2503, which supplies instructions toindividual components of graphics pipeline 2520 or media pipeline 2530.

In some embodiments, command streamer 2503 directs the operation of avertex fetcher 2505 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 2503. In someembodiments, vertex fetcher 2505 provides vertex data to a vertex shader2507, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 2505 andvertex shader 2507 execute vertex-processing instructions by dispatchingexecution threads to execution units 2552A, 2552B via a threaddispatcher 2531.

In some embodiments, execution units 2552A, 2552B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 2552A, 2552B have anattached L1 cache 2551 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 2520 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 2511 configures thetessellation operations. A programmable domain shader 2517 providesback-end evaluation of tessellation output. A tessellator 2513 operatesat the direction of hull shader 2511 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 2520. Insome embodiments, if tessellation is not used, tessellation components2511, 2513, 2517 can be bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 2519 via one or more threads dispatched to executionunits 2552A, 2552B, or can proceed directly to the clipper 2529. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader2519 receives input from the vertex shader 2507. In some embodiments,geometry shader 2519 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 2529 processes vertex data. The clipper2529 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 2573 in the render output pipeline2570 dispatches pixel shaders to convert the geometric objects intotheir per pixel representations. In some embodiments, pixel shader logicis included in thread execution logic 2550. In some embodiments, anapplication can bypass rasterization and access un-rasterized vertexdata via a stream out unit 2523.

The graphics processor 2500 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 2552A, 2552B and associated cache(s) 2551,texture and media sampler 2554, and texture/sampler cache 2558interconnect via a data port 2556 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 2554, caches 2551, 2558 and execution units2552A, 2552B each have separate memory access paths.

In some embodiments, render output pipeline 2570 contains a rasterizerand depth test component 2573 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, the renderoutput pipeline 2570 includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache2578 and depth cache 2579 are also available in some embodiments. Apixel operations component 2577 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 2541, or substituted at display time by the displaycontroller 2543 using overlay display planes. In some embodiments, ashared L3 cache 2575 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 2530 includes amedia engine 2537 and a video front end 2534. In some embodiments, videofront end 2534 receives pipeline commands from the command streamer2503. In some embodiments, media pipeline 2530 includes a separatecommand streamer. In some embodiments, video front-end 2534 processesmedia commands before sending the command to the media engine 2537. Insome embodiments, media engine 2537 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic2550 via thread dispatcher 2531.

In some embodiments, graphics processor 2500 includes a display engine2540. In some embodiments, display engine 2540 is external to processor2500 and couples with the graphics processor via the ring interconnect2502, or some other interconnect bus or fabric. In some embodiments,display engine 2540 includes a 2D engine 2541 and a display controller2543. In some embodiments, display engine 2540 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 2543 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 2520 and media pipeline 2530 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL) and Open Computing Language (OpenCL)from the Khronos Group, the Direct3D library from the MicrosoftCorporation, or support may be provided to both OpenGL and D3D. Supportmay also be provided for the Open Source Computer Vision Library(OpenCV). A future API with a compatible 3D pipeline would also besupported if a mapping can be made from the pipeline of the future APIto the pipeline of the graphics processor.

Graphics Pipeline Programming

FIG. 26A is a block diagram illustrating a graphics processor commandformat 2600 according to some embodiments. FIG. 26B is a block diagramillustrating a graphics processor command sequence 2610 according to anembodiment. The solid lined boxes in FIG. 26A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 2600 of FIG. 26A includes data fields to identify atarget client 2602 of the command, a command operation code (opcode)2604, and the relevant data 2606 for the command. A sub-opcode 2605 anda command size 2608 are also included in some commands.

In some embodiments, client 2602 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 2604 and, if present, sub-opcode 2605 to determine theoperation to perform. The client unit performs the command usinginformation in data field 2606. For some commands an explicit commandsize 2608 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments, commands are aligned via multiples of a double word.

The flow diagram in FIG. 26B shows an exemplary graphics processorcommand sequence 2610. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 2610 maybegin with a pipeline flush command 2612 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 2622 and the media pipeline 2624 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 2612 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 2613 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 2613is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command is 2612 isrequired immediately before a pipeline switch via the pipeline selectcommand 2613.

In some embodiments, a pipeline control command 2614 configures agraphics pipeline for operation and is used to program the 3D pipeline2622 and the media pipeline 2624. In some embodiments, pipeline controlcommand 2614 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 2614 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, commands for the return buffer state 2616 are usedto configure a set of return buffers for the respective pipelines towrite data. Some pipeline operations require the allocation, selection,or configuration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments,configuring the return buffer state 2616 includes selecting the size andnumber of return buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 2620,the command sequence is tailored to the 3D pipeline 2622 beginning withthe 3D pipeline state 2630, or the media pipeline 2624 beginning at themedia pipeline state 2640.

The commands for the 3D pipeline state 2630 include 3D state settingcommands for vertex buffer state, vertex element state, constant colorstate, depth buffer state, and other state variables that are to beconfigured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based the particular 3DAPI in use. In some embodiments, 3D pipeline state 2630 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 2632 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 2632 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 2632command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 2632 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 2622 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 2622 is triggered via an execute 2634command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 2610follows the media pipeline 2624 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 2624 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general-purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 2624 is configured in a similarmanner as the 3D pipeline 2622. A set of commands to configure the mediapipeline state 2640 are dispatched or placed into a command queue beforethe media object commands 2642. In some embodiments, commands for themedia pipeline state 2640 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 2640 also support the use of oneor more pointers to “indirect” state elements that contain a batch ofstate settings.

In some embodiments, media object commands 2642 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 2642. Once the pipeline state is configured andmedia object commands 2642 are queued, the media pipeline 2624 istriggered via an execute command 2644 or an equivalent execute event(e.g., register write). Output from media pipeline 2624 may then be postprocessed by operations provided by the 3D pipeline 2622 or the mediapipeline 2624. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 27 illustrates exemplary graphics software architecture for a dataprocessing system 2700 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application2710, an operating system 2720, and at least one processor 2730. In someembodiments, processor 2730 includes a graphics processor 2732 and oneor more general-purpose processor core(s) 2734. The graphics application2710 and operating system 2720 each execute in the system memory 2750 ofthe data processing system.

In some embodiments, 3D graphics application 2710 contains one or moreshader programs including shader instructions 2712. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 2714 in a machinelanguage suitable for execution by the general-purpose processor core(s)2734. The application also includes graphics objects 2716 defined byvertex data.

In some embodiments, operating system 2720 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 2720 can support agraphics API 2722 such as the Direct3D API or the OpenGL API. When theDirect3D API is in use, the operating system 2720 uses a front-endshader compiler 2724 to compile any shader instructions 2712 in HLSLinto a lower-level shader language. The compilation may be ajust-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 2710.

In some embodiments, user mode graphics driver 2726 contains a back-endshader compiler 2727 to convert the shader instructions 2712 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 2712 in the GLSL high-level language are passed to a usermode graphics driver 2726 for compilation. In some embodiments, usermode graphics driver 2726 uses operating system kernel mode functions2728 to communicate with a kernel mode graphics driver 2729. In someembodiments, kernel mode graphics driver 2729 communicates with graphicsprocessor 2732 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 28 is a block diagram illustrating an IP core development system2800 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system2800 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility2830 can generate a software simulation 2810 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation2810 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 2812. The simulation model 2812 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 2815 can then be created or synthesized from thesimulation model 2812. The RTL design 2815 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 2815, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 2815 or equivalent may be further synthesized by thedesign facility into a hardware model 2820, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 2865 using non-volatile memory 2840 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 2850 or wireless connection 2860. Thefabrication facility 2865 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIGS. 29-31 illustrate exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general purpose processor cores.

FIG. 29 is a block diagram illustrating an exemplary system on a chipintegrated circuit 2900 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 2900includes one or more application processor(s) 2905 (e.g., CPUs), atleast one graphics processor 2910, and may additionally include an imageprocessor 2915 and/or a video processor 2920, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 2900 includes peripheral or bus logic including a USBcontroller 2925, UART controller 2930, an SPI/SDIO controller 2935, andan I²S/I²C controller 2940. Additionally, the integrated circuit caninclude a display device 2945 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 2950 and a mobileindustry processor interface (MIPI) display interface 2955. Storage maybe provided by a flash memory subsystem 2960 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 2965 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine2970.

FIG. 30 is a block diagram illustrating an exemplary graphics processor3010 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 3010 can be a variant of the graphics processor 2910 of FIG.29 . Graphics processor 3010 includes a vertex processor 3005 and one ormore fragment processor(s) 3015A-3015N (e.g., 3015A, 3015B, 3015C,3015D, through 3015N-1, and 3015N). Graphics processor 3010 can executedifferent shader programs via separate logic, such that the vertexprocessor 3005 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 3015A-3015Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 3005 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 3015A-3015N use the primitiveand vertex data generated by the vertex processor 3005 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 3015A-3015N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 3010 additionally includes one or more memorymanagement units (MMUs) 3020A-3020B, cache(s) 3025A-3025B, and circuitinterconnect(s) 3030A-3030B. The one or more MMU(s) 3020A-3020B providefor virtual to physical address mapping for graphics processor 3010,including for the vertex processor 3005 and/or fragment processor(s)3015A-3015N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 3025A-3025B. In one embodiment, the one or more MMU(s)3020A-3020B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 2905, image processor 2915, and/or video processor 2920 ofFIG. 29 , such that each processor 2905-2920 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 3030A-3030B enable graphics processor 3010 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 31 is a block diagram illustrating an additional exemplary graphicsprocessor 3110 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 3110 can be a variant of the graphics processor 2910of FIG. 29 . Graphics processor 3110 includes the one or more MMU(s)3020A-3020B, cache(s) 3025A-3025B, and circuit interconnect(s)3030A-3030B of the integrated circuit 3000 of FIG. 30 .

Graphics processor 3110 includes one or more shader core(s) 3115A-3115N(e.g., 3115A, 3115B, 3115C, 3115D, 3115E, 3115F, through 3015N-1, and3015N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 3110 includes an inter-core taskmanager 3105, which acts as a thread dispatcher to dispatch executionthreads to one or more shader core(s) 3115A-3115N. Graphics processor3110 additionally includes a tiling unit 3118 to accelerate tilingoperations for tile-based rendering, in which rendering operations for ascene are subdivided in image space. Tile-based rendering can be used toexploit local spatial coherence within a scene or to optimize use ofinternal caches.

References to “one embodiment”, “an embodiment”, “example embodiment”,“various embodiments”, etc., indicate that the embodiment(s) sodescribed may include particular features, structures, orcharacteristics, but not every embodiment necessarily includes theparticular features, structures, or characteristics. Further, someembodiments may have some, all, or none of the features described forother embodiments.

In the foregoing specification, embodiments have been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of embodiments asset forth in the appended claims. The Specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

In the following description and claims, the term “coupled” along withits derivatives, may be used. “Coupled” is used to indicate that two ormore elements co-operate or interact with each other, but they may ormay not have intervening physical or electrical components between them.

As used in the claims, unless otherwise specified the use of the ordinaladjectives “first”, “second”, “third”, etc., to describe a commonelement, merely indicate that different instances of like elements arebeing referred to, and are not intended to imply that the elements sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

The following clauses and/or examples pertain to further embodiments orexamples. Specifics in the examples may be used anywhere in one or moreembodiments. The various features of the different embodiments orexamples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperform acts of the method, or of an apparatus or system forfacilitating hybrid communication according to embodiments and examplesdescribed herein.

Some embodiments pertain to Example 1 that includes an apparatus tofacilitate learning and application of neural network topologies inmachine learning at autonomous machines, the apparatus comprising:detection/observation logic, as facilitated by or at least partiallyincorporated into a processor, to monitor and detect structure learningof neural networks relating to machine learning operations at theapparatus having the processor; generative model logic, as facilitatedby or at least partially incorporated into the processor, to generate arecursive generative model based on one or more topologies of one ormore of the neural networks; and discriminative model logic, asfacilitated by or at least partially incorporated into the processor, toconvert the generative model into a discriminative model.

Example 2 includes the subject matter of Example 1, wherein thegenerative model is unsupervised and based on unlabeled data, andwherein the discriminative mode is supervised and based on labeled data,wherein the discriminative model is learned from the generative model.

Example 3 includes the subject matter of Examples 1-2, wherein thediscriminative model logic is further to inverse the generative modelinto multiple inverse models, wherein a bidirectional connection isadded to connect latent variables having a common parent in each of themultiple inverse models to consolidate the multiple inverse models intoa single inverse model.

Example 4 includes the subject matter of Examples 1-3, wherein thediscriminative model logic is further to convert the inverse model intothe discriminative model by removing the bidirectional connection andadding a class node serving as a child node to latent leaves.

Example 5 includes the subject matter of Examples 1-4, furthercomprising: dropout logic, as facilitated by or at least partiallyincorporated into the processor, to perform methodological dropout ofneurons from one or more of the neural networks, wherein themethodological dropout is performed in accordance with a predictivitybased on historical statistical data relating to the neurons;decomposition logic, as facilitated by or at least partiallyincorporated into the processor, to generate parallel and sequentialexecution schedules for memory sharing at sub-network precision levelsof the one or more of the neural networks; and on-the-flylearning/update logic, as facilitated by or at least partiallyincorporated into the processor, to perform on-the-fly learning andupdating of network topologies of the neural networks based on at leastone of currently available data and historically available data relatingto the topologies of the neural networks.

Example 6 includes the subject matter of Examples 1-5, furthercomprising: structure learning logic, as facilitated by or at leastpartially incorporated into the processor, to facilitate at least one ofan end-to-end structure learning and a sub-network structure learning;and training and feature logic, as facilitated by or at least partiallyincorporated into the processor, to facilitate feature bagging or copingwith large scale data by training large training sets.

Example 7 includes the subject matter of Examples 1-6, wherein theprocessor comprises a graphics processor co-located with an applicationprocessor on a common semiconductor package.

Some embodiments pertain to Example 8 that includes a method forfacilitating memory handling and data management in machine learning atautonomous machines, the method comprising: monitoring and detectingstructure learning of neural networks relating to machine learningoperations at a computing device having a processor; generating arecursive generative model based on one or more topologies of one ormore of the neural networks; and converting the generative model into adiscriminative model.

Example 9 includes the subject matter of Example 8, wherein thegenerative model is unsupervised and based on unlabeled data, andwherein the discriminative mode is supervised and based on labeled data,wherein the discriminative model is learned from the generative model.

Example 10 includes the subject matter of Examples 8-9, furthercomprising: inversing the generative model into multiple inverse models,wherein a bidirectional connection is added to connect latent variableshaving a common parent in each of the multiple inverse models toconsolidate the multiple inverse models into a single inverse model.

Example 11 includes the subject matter of Examples 8-10, furthercomprising: converting the inverse model into the discriminative modelby removing the bidirectional connection and adding a class node servingas a child node to latent leaves.

Example 12 includes the subject matter of Examples 8-11, furthercomprising: performing methodological dropout of neurons from one ormore of the neural networks, wherein the methodological dropout isperformed in accordance with a predictivity based on historicalstatistical data relating to the neurons; generating parallel andsequential execution schedules for memory sharing at sub-networkprecision levels of the one or more of the neural networks; andperforming on-the-fly learning and updating of network topologies of theneural networks based on at least one of currently available data andhistorically available data relating to the topologies of the neuralnetworks.

Example 13 includes the subject matter of Examples 8-12, furthercomprising: facilitating at least one of an end-to-end structurelearning and a sub-network structure learning; and facilitating featurebagging or coping with large scale data by training large training sets.

Example 14 includes the subject matter of Examples 8-13, wherein theprocessor comprises a graphics processor co-located with an applicationprocessor on a common semiconductor package.

Some embodiments pertain to Example 15 that includes a graphicsprocessing system comprising a computing device having memory coupled toa processor, the processor to: monitor and detect structure learning ofneural networks relating to machine learning operations; generate arecursive generative model based on one or more topologies of one ormore of the neural networks; and convert the generative model into adiscriminative model.

Example 16 includes the subject matter of Example 15, wherein thegenerative model is unsupervised and based on unlabeled data, andwherein the discriminative mode is supervised and based on labeled data,wherein the discriminative model is learned from the generative model.

Example 17 includes the subject matter of Example 15-16, wherein theprocessor is further to: inverse the generative model into multipleinverse models, wherein a bidirectional connection is added to connectlatent variables having a common parent in each of the multiple inversemodels to consolidate the multiple inverse models into a single inversemodel.

Example 18 includes the subject matter of Example 15-17, furthercomprising: convert the inverse model into the discriminative model byremoving the bidirectional connection and adding a class node serving asa child node to latent leaves.

Example 19 includes the subject matter of Examples 15-18, furthercomprising: perform methodological dropout of neurons from one or moreof the neural networks, wherein the methodological dropout is performedin accordance with a predictivity based on historical statistical datarelating to the neurons; generate parallel and sequential executionschedules for memory sharing at sub-network precision levels of the oneor more of the neural networks; and perform on-the-fly learning andupdating of network topologies of the neural networks based on at leastone of currently available data and historically available data relatingto the topologies of the neural networks.

Example 20 includes the subject matter of Examples 15-19, furthercomprising: facilitate at least one of an end-to-end structure learningand a sub-network structure learning; and facilitate feature bagging orcoping with large scale data by training large training sets.

Example 21 includes the subject matter of Examples 15-20, wherein theprocessor comprises a graphics processor co-located with an applicationprocessor on a common semiconductor package.

Example 22 includes at least one non-transitory or tangiblemachine-readable medium comprising a plurality of instructions, whenexecuted on a computing device, to implement or perform a method asclaimed in any of claims or examples 8-14.

Example 23 includes at least one machine-readable medium comprising aplurality of instructions, when executed on a computing device, toimplement or perform a method as claimed in any of claims or examples8-14.

Example 24 includes a system comprising a mechanism to implement orperform a method as claimed in any of claims or examples 8-14.

Example 25 includes an apparatus comprising means for performing amethod as claimed in any of claims or examples 8-14.

Example 26 includes a computing device arranged to implement or performa method as claimed in any of claims or examples 8-14.

Example 27 includes a communications device arranged to implement orperform a method as claimed in any of claims or examples 8-14.

Example 28 includes at least one machine-readable medium comprising aplurality of instructions, when executed on a computing device, toimplement or perform a method or realize an apparatus as claimed in anypreceding claims.

Example 29 includes at least one non-transitory or tangiblemachine-readable medium comprising a plurality of instructions, whenexecuted on a computing device, to implement or perform a method orrealize an apparatus as claimed in any preceding claims.

Example 30 includes a system comprising a mechanism to implement orperform a method or realize an apparatus as claimed in any precedingclaims.

Example 31 includes an apparatus comprising means to perform a method asclaimed in any preceding claims.

Example 32 includes a computing device arranged to implement or performa method or realize an apparatus as claimed in any preceding claims.

Example 33 includes a communications device arranged to implement orperform a method or realize an apparatus as claimed in any precedingclaims.

The drawings and the forgoing description give examples of embodiments.Those skilled in the art will appreciate that one or more of thedescribed elements may well be combined into a single functionalelement. Alternatively, certain elements may be split into multiplefunctional elements. Elements from one embodiment may be added toanother embodiment. For example, orders of processes described hereinmay be changed and are not limited to the manner described herein.Moreover, the actions of any flow diagram need not be implemented in theorder shown; nor do all of the acts necessarily need to be performed.Also, those acts that are not dependent on other acts may be performedin parallel with the other acts. The scope of embodiments is by no meanslimited by these specific examples. Numerous variations, whetherexplicitly given in the specification or not, such as differences instructure, dimension, and use of material, are possible. The scope ofembodiments is at least as broad as given by the following claims.

1. An apparatus comprising: a graphics processor to: learn a structureof a generative probabilistic model; determine, based on the structure,a stochastic inverse of the generative probabilistic model; convert thestochastic inverse into a discriminative model; convert thediscriminative model into a deep neural network (DNN) that is trainedusing labeled data; and train the DNN using labeled data, whereintraining the DNN comprises at least: setting a bit-precision of weightsare set in neurons of the DNN independently from one another; andperforming methodological dropout of the neurons, wherein themethodological dropout is performed in accordance with a predictivitybased on historical statistical data relating to the neurons.
 2. Theapparatus of claim 1, wherein the generative model is unsupervised andbased on unlabeled data, and wherein the discriminative model issupervised and based on labeled data, wherein the discriminative modelis learned from the generative probabilistic model.
 3. The apparatus ofclaim 1, wherein the graphics processor is further to inverse thegenerative probabilistic model into multiple inverse models, wherein abidirectional connection is added to connect latent variables having acommon parent in each of the multiple inverse models to consolidate themultiple inverse models into a single inverse model.
 4. The apparatus ofclaim 3, wherein the graphics processor is further to convert theinverse model into the discriminative model by removing thebidirectional connection and adding a class node serving as a child nodeto latent leaves.
 5. The apparatus of claim 1, wherein the graphicsprocessor is further to: generate parallel and sequential executionschedules for memory sharing at sub-network precision levels of the oneor more of the neural networks; and perform on-the-fly learning andupdating of network topologies of the neural networks based on at leastone of currently available data and historically available data relatingto the topologies of the neural networks.
 6. The apparatus of claim 1,wherein the graphics processor is further to: facilitate at least one ofan end-to-end structure learning and a sub-network structure learning;and facilitate feature bagging or coping with large scale data bytraining large training sets.
 7. The apparatus of claim 1, wherein thegraphics processor is co-located with an application processor on acommon semiconductor package.
 8. A method comprising: learning, by agraphics processor, a structure of a generative probabilistic model;determining, based on the structure, a stochastic inverse of thegenerative probabilistic model; converting the stochastic inverse into adiscriminative model; converting the discriminative model into a deepneural network (DNN) that is trained using labeled data; and trainingthe DNN using labeled data, wherein training the DNN comprises at least:setting a bit-precision of weights are set in neurons of the DNNindependently from one another; and performing methodological dropout ofthe neurons, wherein the methodological dropout is performed inaccordance with a predictivity based on historical statistical datarelating to the neurons.
 9. The method of claim 8, wherein thegenerative probabilistic model is unsupervised and based on unlabeleddata, and wherein the discriminative model is supervised and based onlabeled data, wherein the discriminative model is learned from thegenerative probabilistic model.
 10. The method of claim 8, furthercomprising inversing the generative probabilistic model into multipleinverse models, wherein a bidirectional connection is added to connectlatent variables having a common parent in each of the multiple inversemodels to consolidate the multiple inverse models into a single inversemodel.
 11. The method of claim 10, further comprising converting theinverse model into the discriminative model by removing thebidirectional connection and adding a class node serving as a child nodeto latent leaves.
 12. The method of claim 8, further comprising:generating parallel and sequential execution schedules for memorysharing at sub-network precision levels of the one or more of the neuralnetworks; and performing on-the-fly learning and updating of networktopologies of the neural networks based on at least one of currentlyavailable data and historically available data relating to thetopologies of the neural networks.
 13. The method of claim 8, furthercomprising: facilitating at least one of an end-to-end structurelearning and a sub-network structure learning; and facilitating featurebagging or coping with large scale data by training large training sets.14. The method of claim 8, wherein the graphics processor is co-locatedwith an application processor on a common semiconductor package.
 15. Atleast one non-transitory machine-readable medium comprising instructionsthat when executed by a computing device, cause the computing device toperform operations comprising: learning, by a graphics processor of thecomputing device, a structure of a generative probabilistic model;determining, based on the structure, a stochastic inverse of thegenerative probabilistic model; converting the stochastic inverse into adiscriminative model; converting the discriminative model into a deepneural network (DNN) that is trained using labeled data; and trainingthe DNN using labeled data, wherein training the DNN comprises at least:setting a bit-precision of weights are set in neurons of the DNNindependently from one another; and performing methodological dropout ofthe neurons, wherein the methodological dropout is performed inaccordance with a predictivity based on historical statistical datarelating to the neurons.
 16. The non-transitory machine-readable mediumof claim 15, wherein the generative probabilistic model is unsupervisedand based on unlabeled data, and wherein the discriminative model issupervised and based on labeled data, wherein the discriminative modelis learned from the generative probabilistic model.
 17. Thenon-transitory machine-readable medium of claim 15, wherein theoperations further comprise inversing the generative probabilistic modelinto multiple inverse models, wherein a bidirectional connection isadded to connect latent variables having a common parent in each of themultiple inverse models to consolidate the multiple inverse models intoa single inverse model.
 18. The non-transitory machine-readable mediumof claim 17, wherein the operations further comprise converting theinverse model into the discriminative model by removing thebidirectional connection and adding a class node serving as a child nodeto latent leaves.
 19. The non-transitory machine-readable medium ofclaim 15, wherein the operations further comprise: generating paralleland sequential execution schedules for memory sharing at sub-networkprecision levels of the one or more of the neural networks; andperforming on-the-fly learning and updating of network topologies of theneural networks based on at least one of currently available data andhistorically available data relating to the topologies of the neuralnetworks.
 20. The non-transitory machine-readable medium of claim 15,wherein the operations further comprise: facilitating at least one of anend-to-end structure learning and a sub-network structure learning; andfacilitating feature bagging or coping with large scale data by traininglarge training sets, wherein the processor comprises a graphicsprocessor co-located with an application processor on a commonsemiconductor package.
 21. A system comprising: a memory; and a graphicsprocessor communicably coupled to the memory, the graphics processor to:learn a structure of a generative probabilistic model; determine, basedon the structure, a stochastic inverse of the generative probabilisticmodel; convert the stochastic inverse into a discriminative model;convert the discriminative model into a deep neural network (DNN) thatis trained using labeled data; and train the DNN using labeled data,wherein training the DNN comprises at least: setting a bit-precision ofweights are set in neurons of the DNN independently from one another;and performing methodological dropout of the neurons, wherein themethodological dropout is performed in accordance with a predictivitybased on historical statistical data relating to the neurons.
 22. Thesystem of claim 21, wherein the generative model is unsupervised andbased on unlabeled data, and wherein the discriminative model issupervised and based on labeled data, wherein the discriminative modelis learned from the generative probabilistic model.
 23. The system ofclaim 21, wherein the graphics processor is further to inverse thegenerative probabilistic model into multiple inverse models, wherein abidirectional connection is added to connect latent variables having acommon parent in each of the multiple inverse models to consolidate themultiple inverse models into a single inverse model.
 24. The system ofclaim 23, wherein the graphics processor is further to convert theinverse model into the discriminative model by removing thebidirectional connection and adding a class node serving as a child nodeto latent leaves.
 25. The system of claim 21, wherein the graphicsprocessor is further to: generate parallel and sequential executionschedules for memory sharing at sub-network precision levels of the oneor more of the neural networks; and perform on-the-fly learning andupdating of network topologies of the neural networks based on at leastone of currently available data and historically available data relatingto the topologies of the neural networks.